From: Ananda Kumar
Date: June 14 2012 10:03am
Subject: Re: NoSQL help
List-Archive: http://lists.mysql.com/mysql/227654
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can u share the sql, explain plan, indexes etc,
show full processlist out put when the sql's are running
On Thu, Jun 14, 2012 at 3:03 PM, Manivannan S. <
manivannan_s@stripped> wrote:
> I tried with myisam engine also. But it also taking more time to generate
> the report. In my database I am having 8 innodb tables and at the same time
> I am joining 4 tables to get the report.
>
> I am maintaining 60days records because the user will try to generate the
> report out of 60 days in terms of second, minute, hourly, weekly and
> Monthly report also.
>
> From: Ananda Kumar [mailto:anandkl@stripped]
> Sent: Thursday, June 14, 2012 12:32 AM
> To: Rick James
> Cc: Johan De Meersman; Manivannan S.; mysql@stripped
> Subject: Re: NoSQL help
>
> Did you try with myisam tables.
> They are supposed to be good for reporting requirement
> On Wed, Jun 13, 2012 at 11:52 PM, Rick James rjames@stripped>> wrote:
> I'll second Johan's comments.
>
> "Count the disk hits!"
>
> One minor change: Don't store averages in the summary table; instead
> store the SUM(). That lets you get the mathematically correct AVERAGE over
> any time range via
> SUM(sum_foo) / SUM(count_foo)
>
> Switching between MySQL and Mongo requires rewriting _all_ of the relevant
> code.
>
> NoSQL will be no better than MySQL for 150GB. "Count
> the disk hits!"
>
> I recently built a system that topped out at 350GB (90 days' data). It
> involved hourly ingestion of a few GB of data and a variety of "reports".
> The prototype showed that most reports would take about an hour to run.
> Not good. The final product, with summary tables, lets the reports be run
> on-demand and online and each takes only a few seconds. By careful use of
> MEMORY tables, LOAD DATA, etc, the ingestion takes 5 minutes (each hour)
> for the raw data and 2 minutes (total) for the 7 summary tables.
> PARTITIONing was vital for the design. Once an hour a new partition is
> populated; once a day, 24 hourly partitions are rolled into a new daily
> partition and the 90-day old partition is DROPped.
>
>
> > -----Original Message-----
> > From: Johan De Meersman [mailto:vegivamp@stripped vegivamp@stripped>]
> > Sent: Wednesday, June 13, 2012 6:20 AM
> > To: Manivannan S.
> > Cc: mysql@stripped
> > Subject: Re: NoSQL help
> >
> >
> > ----- Original Message -----
> > > From: "Manivannan S." manivannan_s@stripped>>
> > >
> > > Hi all,
> > >
> > > [lots of data]
> > > [slow reports]
> > > [wooo NoSQL magic]
> >
> > Not that I want to discourage you, but my standard first question is
> > "why do you think NoSQL (let alone any specific product) is the right
> > solution?" :-)
> >
> > Don't get me wrong, it might be; but from what little I now know about
> > your environment, it sounds like applying some data warehousing
> > techniques might suffice - and being the cynical dinosaur that I am, I
> > have a healthy reluctance about welding new technology onto a stable
> > environment.
> >
> > To speed up reporting (and note that these techniques are often applied
> > even when implementing NoSQL solutions, too) it is usually a good first
> > step to set up a process of data summarization.
> >
> > Basically, you pre-calculate averages, medians, groupings, whatever you
> > need for your reports; and your job also saves the last record IDs it's
> > processed; then on the next run, you only read the new records and
> > update your summary tables to incorporate the new data.
> >
> > Suppose I have a table like this:
> >
> > ID | Val
> > --------
> > 1 1
> > 2 7
> > 3 5
> > 4 13
> >
> > I want to report the average on a daily basis, and calculating that
> > over those rows is unbearably slow because I'm running the process on a
> > wristwatch from 1860 :-)
> >
> > So I get a summary table, calculate (1+7+5+13)/4 = 6.5 and that then
> > gets a record saying this:
> >
> > Avg | elementCount | lastSeen
> > -----------------------------
> > 6.5 4 4
> >
> > Now, over the course of the day, the elements 4, 17 and 2 get added
> > with sequential row numbers. Instead of calculating
> > (1+7+5+13+4+17+2)/7, which would be slow; I can substitute the already
> > summarized data by Avg*elementCount. Thus, I calculate (6.5*4 +
> > 4+17+2)/7 = 7, which is a lot faster, and my summary table now looks
> > like this:
> >
> > Avg | elementCount | lastSeen
> > -----------------------------
> > 7 7 7
> >
> > This is of course a stupid example, but it saves you a lot of time if
> > you already have the summary of several thousand elements and only need
> > to update it for a handful. Similar tricks are possible for a lot of
> > typical reporting stuff - you don't need to re-calculate data for past
> > months over and over again, for instance - and that's what makes your
> > reports run fast.
> >
> >
> > Just my 2 cents :-)
> > /johan
> >
> > --
> > Bier met grenadyn
> > Is als mosterd by den wyn
> > Sy die't drinkt, is eene kwezel
> > Hy die't drinkt, is ras een ezel
> >
> > --
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